seedream-mcp

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. 'Look up' implies a safe read, but the description never confirms it has no side effects, doesn't mention auth requirements, caching, or billing context, and says nothing about the shape of the pricing returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler. The resource and scope appear immediately, which is exactly what a lookup tool needs.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-optional-parameter tool with no annotations and no output schema, the description is adequate but thin. It doesn't clarify what the pricing response contains or how 'model line' maps to the model/action parameters, leaving the agent to infer the return shape.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters (model, action) are documented in the schema, including their defaults and the action enum. The description's reference to 'the seedream model line' loosely aligns with the model parameter but adds no new syntax or format guidance. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('look up') and resource ('RunAPI pricing') scoped to the seedream model line. It's clear what the tool returns, though it doesn't explicitly contrast itself with the sibling action tools (decompose_layers, edit_image, text_to_image), which is understandable given they're a different class of operation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No indication of when to call this versus alternatives, no prerequisites, and no mention of whether it should precede a priced action. The agent gets the what but not the when.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It usefully discloses the return shape (task id, status, output URLs), which matters because there is no output schema, but it omits async/polling behavior implied by the 'wait' parameter, cost implications, and auth requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short, front-loaded sentences with no filler. The parenthetical '(decompose layers)' is slightly redundant with the tool name, but overall it is tight.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 9-parameter task-creation tool with no annotations and no output schema, the description covers the return shape but leaves async behavior, cost, and failure/validation conditions unstated. Adequate but with clear gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 78%, so most parameters are already documented in the schema. The description adds no parameter-level meaning (e.g., what 'prompt' controls, what 'size' affects), so baseline 3 applies for high-coverage schemas.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a verb (Create) and an object (a Seedream task on RunAPI), and the parenthetical repeats the tool name rather than explaining what locating/decomposing layers produces. An agent can tell it is image-layer separation, but not what the output layers mean or how it differs functionally from edit_image.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this tool versus the siblings (edit_image, text_to_image) or any prerequisite conditions. The only implicit cue is the name itself, which is not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose that the call is asynchronous and returns a task id, status, and output URLs. It omits polling/terminal-status behavior implied by the wait parameter, authentication or cost implications, and the effect of enable_safety_checker.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with the action front-loaded and zero filler. The size is tight, though the brevity contributes to the missing operational context rather than being purely optimal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 14-parameter tool with no annotations and no output schema, the description is thin: it never explains the wait/polling contract, how source_image_urls and prompt combine, or which parameters are essential. It names return fields, which is the one thing output-schema absence makes valuable, but leaves most operational requirements unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 86%, so the schema already documents parameters such as wait, model, and aspect_ratio. The description adds no parameter-level meaning at all, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Create a Seedream task on RunAPI') and tags the mode with '(edit image)', which separates it from the sibling text_to_image. The framing as task creation rather than an image-editing action is slightly indirect, but an agent can identify what it does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to pick this over text_to_image, nor any mention of the async pattern (wait defaults to true; set it false and poll with get_task). The sibling get_task exists precisely for the non-waiting path and the description never routes the agent there.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the full behavioral burden. It does disclose return content (status plus latest result payload) and, via "latest," hints the value evolves over time, but omits key traits for an async poll: whether results are only present once complete, typical polling cadence, and error behavior for unknown task ids.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence that front-loads the returned artifacts (status, result payload) with no filler. Nothing could be trimmed without losing meaning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter tool with 100% schema coverage this is close to adequate, and the description partially compensates for the absent output schema by naming the return contents. It still lacks the status vocabulary and completion semantics an agent needs to interpret responses correctly during polling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% – both task_id and action (with its enum) are documented in the schema, and the enum values map to the sibling creation endpoints. The description adds no parameter-level detail beyond the schema, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb ("Fetch") and resource ("current status and latest result payload for a seedream task"), so the operation is unambiguous. It does not, however, explicitly connect itself to its sibling async-creation tools (decompose_layers, edit_image, text_to_image), leaving the caller to infer the create-then-poll relationship.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: an agent can infer this polls a task created by a sibling endpoint, but the description never states when to call it (e.g., after a create call, or repeatedly until completion), nor does it name an alternative or exclusion. No explicit guidance is provided, so 3 is the ceiling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden. It discloses the interactive browser flow and the file write side effect (config.json). However, it does not mention that it may overwrite existing credentials or that it could block waiting for user input, though these are implied.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the action ('Authenticate RunAPI') and provides necessary details without extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple login tool with one optional parameter and no output schema, the description covers the core purpose and side effect. It lacks an explicit statement that this is a prerequisite for other tools, but that is implied.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (the only parameter 'force' has a description). The tool description adds no additional meaning about parameters beyond the schema, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Authenticate'), target resource ('RunAPI'), method ('browser PKCE login flow'), and side effect (saving to config.json). It is distinct from sibling tools, none of which relate to authentication.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage (to authenticate RunAPI) but does not explicitly say when to run it (e.g., before other RunAPI tools) or when to use the 'force' parameter. Since there are no alternative auth tools among siblings, 'vs alternatives' is not applicable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden, and it does disclose that this is an asynchronous task creation returning a task id, status, and output URLs. However, it omits the default polling behavior implied by the wait parameter, any cost/credit implications (notable given the check_pricing sibling), and failure modes. Some value added, but significant gaps remain for a 13-parameter task-creation tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with the action front-loaded and the return values stated second. Nothing is wasted, though the parenthetical '(text to image)' slightly duplicates the tool name.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 13-parameter, zero-required, async task tool with no annotations and no output schema, the description covers purpose and return shape but leaves out cost, authentication needs, and how the wait/poll defaults behave. Adequate for a basic call, but not complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is high (85%), so the schema already documents most inputs; the description adds no parameter-level detail such as prompt syntax, effect of output_count, or the wait/polling interaction. Baseline 3 is appropriate when the schema does the heavy lifting and the description contributes nothing extra.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Create a Seedream task on RunAPI') and clarifies the modality with '(text to image)', which distinguishes it from the edit_image sibling. It stops short of explicitly naming when this tool is preferred over edit_image or decompose_layers, but the purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use or when-not-to-use guidance, no mention of prerequisites (API key/login), and no routing to alternatives such as edit_image for modifying existing images. The agent must infer usage entirely from the tool name and 'text to image' parenthetical.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.